wehub-resource-sync
62 lines
2.0 KiB
C++
62 lines
2.0 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T, typename Context>
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void PixelShuffleGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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int upscale_factor,
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const std::string& data_format,
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DenseTensor* x_grad) {
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auto* dout = &out_grad;
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auto* dx = x_grad;
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dev_ctx.template Alloc<T>(dx);
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if (dx && dx->numel() == 0) {
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return;
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}
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int factor = upscale_factor;
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bool channel_last = (data_format == "NHWC");
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const auto& do_dims = dout->dims();
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const auto& dx_dims = dx->dims();
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DenseTensor t(*dout);
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if (!channel_last) {
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t.Resize({do_dims[0], do_dims[1], dx_dims[2], factor, dx_dims[3], factor});
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} else {
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t.Resize({do_dims[0], dx_dims[1], factor, dx_dims[2], factor, do_dims[3]});
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}
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std::vector<int> axis = {0, 1, 3, 5, 2, 4};
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DenseTensor o(*dx);
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if (!channel_last) {
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o.Resize({do_dims[0], do_dims[1], factor, factor, dx_dims[2], dx_dims[3]});
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} else {
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o.Resize({do_dims[0], dx_dims[1], dx_dims[2], do_dims[3], factor, factor});
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}
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funcs::Transpose<Context, T, 6> trans;
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trans(dev_ctx, t, &o, axis);
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dx->Resize(dx_dims);
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}
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} // namespace phi
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